How can I improve the accuracy of my PyTorch model after quantization?

How can I improve the accuracy of my PyTorch model after quantization?

To improve accuracy after quantization, consider techniques like fine-tuning, using mixed precision, or adjusting quantization parameters.

What is fine-tuning and how does it help?

Fine-tuning involves retraining your model on a smaller dataset after quantization, which can help recover lost accuracy.

Are there specific layers that are more sensitive to quantization?

Yes, layers like convolutional and fully connected layers can be more sensitive. It's important to monitor their performance.

Can I use quantization-aware training instead?

Absolutely! Quantization-aware training simulates quantization during training, which can lead to better accuracy post-quantization.

What tools can I use for quantization in PyTorch?

You can use PyTorch's built-in quantization tools, such as torch.quantization, which provides various methods for quantization.